Bard College
Bard Digital Commons
Senior Projects Spring 2014 Bard Undergraduate Senior Projects
2014
e Eects of Robots on Computer Science
Perceptions
Shannon Marie Gray
Bard College
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Recommended Citation
Gray, Shannon Marie, "e Eects of Robots on Computer Science Perceptions" (2014). Senior Projects Spring 2014. Paper 251.
hp://digitalcommons.bard.edu/senproj_s2014/251
The Effects of Robots on Computer Science Perceptions
A Senior Project submitted to
The Division of Science, Mathematics and Computing
of Bard College
by
Shannon Gray
Annandale-on-Hudson, New York
May 2014
ABSTRACT
This project examines and analyses the data from several Institute for Personal Robots in
Education (IPRE) studies in 2007, 2009 and 2010 that judged students attitudes of computer
science before and after an introductory computer science course. The course was run in two
conditions: a class involving robots and a class with no robots. The 2007 studies found that
students in the robot classes did 10% better on average in the course than those students in the
non-robots class. The data from the most recent studies show that while robots may increase
performance in the class, they did not seem to have any significant effects on changing attitudes
or perceptions. However, robots did increase the likelihood that students discussed their
assignments and lectures with their peers, a factor that could lead to increasing involvement in
computer science. This senior project aims to take their research further with another follow-up
study that address two research questions. How much computer science can someone actually
learn in an hour, and is an hour enough to change peoples’ perceptions of computer science?
Secondly, what are the differences between using robots to teach computer science versus using
robot simulations? To answer these questions hour-long coding workshops were designed based
on the principles of Hour of Code and the Institute for Personal Robots in Education, which
would introduce participants to programming a Scribbler robot.
CONTENTS
ABSTRACT ..................................................................................................................... 2
CONTENTS ..................................................................................................................... 3
ACKNOWLEDGEMENTS ................................................................................................ 5
1 INTRODUCTION .......................................................................................................... 6
1.1 Motivation ............................................................................................................... 6
1.2 Computer Science Perceptions ................................................................................... 7
1.2.1 Changing Perceptions ......................................................................................... 7
2 INSTITUTE FOR PERSONAL ROBOTS IN EDUCATION ............................................. 10
2.1 Overview ............................................................................................................... 10
2.2 Myro and Scribblers ................................................................................................ 11
2.3 Calico .................................................................................................................... 13
2.4 IPRE Studies .......................................................................................................... 16
3 RELATED WORK ....................................................................................................... 18
3.1 Teaching ................................................................................................................ 18
3.2 Robotics ................................................................................................................ 20
3.3 Robots as Motivators ............................................................................................... 22
3.4 An Hour versus a Semester ...................................................................................... 23
3.5 Robot Simulations .................................................................................................. 24
4 STUDY ANALYSIS ..................................................................................................... 26
4.1 Robotics Class ........................................................................................................ 26
4.2 Non-Robotics Class ................................................................................................ 27
4.3 Methodology .......................................................................................................... 27
4.4 Results ................................................................................................................... 27
4.4.1 General Opinions and Observations .................................................................... 28
4.4.2 Statistically Significant Group Differences .......................................................... 29
4.4.3 Summary ......................................................................................................... 29
5 MOVING FORWARD .................................................................................................. 48
5.1 Methodology .......................................................................................................... 49
5.2 Introduction............................................................................................................ 50
5.3 First Task ............................................................................................................... 50
5.4 Second Task ........................................................................................................... 50
6 CONCLUSION ............................................................................................................ 52
APPENDIX .................................................................................................................... 54
IPRE Results ............................................................................................................... 55
Question Key ........................................................................................................... 55
Tables ..................................................................................................................... 56
IPRE Documents .......................................................................................................... 60
Robots Course Syllabus ............................................................................................. 60
Proposed Study Documents ........................................................................................... 62
Documents Submitted for IRB Approval ..................................................................... 62
Workshop Introduction Materials ............................................................................... 73
Workshop Task Materials ............................................................................................. 79
BIBLIOGRAPHY ........................................................................................................... 81
ACKNOWLEDGEMENTS
The success of any project depends on the support and teachings of many others. I would like to
express my gratitude to the people who have aided in the successful completion of this project.
I would like to thank Keith O’Hara, my advisor since first year and my senior project advisor as
well. Thanks for keeping me from freaking out too often, and getting through this last year.
I would like to thank my family and friends for the support though the years, especially my
mother. You have always been there for me, and it really means the world. So, thanks to all.
6
1
INTRODUCTION
1.1 Motivation
One goal of this project is to examine the differences between classes that use robots to teach
introductory computer science, and classes that do not make use of robots. Does one have a
greater effect on changing computer science perceptions? Is one more interesting than the other,
or does one make a better teaching tool?
This senior project then aims to take their research further with another follow-up study
that address two research questions. How much computer science can someone actually learn in
an hour, and is an hour enough to change peoples’ perceptions of computer science? Secondly,
what are the differences between using robots to teach computer science versus using robot
simulations? Does the physicality of real robots promote learning that is more engaging? Do
simulations make a task more interesting or challenging? What are the differences in these two
teaching tools, and how do they effect perceptions of computer science?
7
A broader goal of this project is to show people that anyone can—and should—do
computer science. It is not as scary as it seems. You do not have to be a technology or math
person. Part of the motivation is changing the reputation that computer science has and getting
more people interested in computer science, an ongoing effort for many in the computer science
field. Computer science and “coding” are not the same thing. Programming is an aspect of
computer science and not everyone ends up a programmer. There are multitudes of fields that
make use of computer science, and everyone could benefit from learning more about it.
[12]
1.2 Computer Science Perceptions
Computer scientists are technology-oriented, with strong interests in programming and
electronics, and little interest in people. They are so focused on technology that they are
obsessed with computers and programming, to the exclusion of other interests. They were “born
coding” or “dream in code”. They lack interpersonal skills and are socially awkward. Computer
scientists are all nerds or geeks. They are mostly unattractive, pale and thin, and wearing
glasses. They are predominately male. These are just some of the common stereotypes that
Cheryan et al. (2013) found in their research. Cheryan et al. (2013) also found that these
stereotypes about computer scientists’ are more prevalent among populations with less
computing experience.
[3]
1.2.1 Changing Perceptions
How do you go about changing the way something is, and has been perceived for many
generations? How do you undo the stigmas associated with Computer Science? One way is to
expose people to computer science; show people what computer science really is. Show them
real people who have studied it. Who you are does not define what you can or cannot do, and
vice versa. Instead of focusing on the typical pale, white male role models we all know, we
8
should learn about people like Marissa Mayer
1
, Sundar Pichai
2
, Elena Silenok
3
, and Ginni
Rometty
4
. They are defying stereotypes and can help inspire others to as well. This is what the
Hour of Code Campaign embodies. Their promotional videos
5
are essentially about how
important computer science is to know, and why everyone should try it.
Hour of Code
6
is an opportunity for every student to try computer science for one hour,
although they encourage learning the Hour of Code all year-round.
[24]
During Computer Science
Education Week (December 8-14, 2014), over thirty three million people participated in the
Hour of Code.
[8]
They have tutorials and ideas for people of all ages to try, that cover a wide
variety of activities: game and app designing, robotics, creating cards and graphics, et cetera.
They even have activities for people that do not have computer access, but can still learn
programming concepts “unplugged”.
[4]
The Hour of Code is not out to educate people on all the ins and outs of computer
science. It is an effort to show people the essence of computer science. They want people to
think about things in their everyday lives that use computer science: a cell phone, a microwave, a
computer, a traffic light, etc. and that all of these things needed a computer scientist to help build
them. Hour of Code aims to show that computer science is the art of blending human ideas and
digital tools to increase our power. That computer scientists work in so many different areas:
writing apps for phones, curing diseases, creating animated movies, working on social media,
building robots that explore other planets and so much more. Again, not everyone who studies
1
Current President and CEO of Yahoo!.
2
Senior vice president at Google. Oversees Android, Chrome and Google Apps.
3
Founder of Clothia, a fashion website and an iPad app that allows users to create virtual wardrobes,
browse their friends’ closets, mix-n-match items to create outfits, get inspired by style icons, share their
finds, and virtually try on clothes using augmented reality via their webcam.
4
Current Chairwoman and CEO of IBM, and the first woman to head the company.
5
http://www.youtube.com/user/CodeOrg
6
https://code.org/
9
computer science ends up a programmer, and they certainly are not all pale, unattractive
antisocial people living in dark basements, despite what the stereotypes say.
10
2
INSTITUTE FOR PERSONAL ROBOTS IN EDUCATION
2.1 Overview
The Institute for Personal Robots in Education (IPRE) aims to change computer science
perceptions and attitudes, and increase the number of people involved in computing. They
believe that the use of personal robots and engaging examples can provide a sound foundation
for learning computing, in addition to attracting a more diverse body of students into the
computing disciplines.
[12]
IPRE was created to make computer science education more fun and
effective through the use of personal robots, and is a joint effort between Georgia Tech and Bryn
Mawr College sponsored by Microsoft Research.
[10]
Robots can provide a tangible and personal
means to engage a student in engineering, science and math education.
Their mission is broad: to
employ robots in education at all levels from middle school to graduate school, though their
initial audience was introductory undergraduate computer science. IPRE aims to improve
retention in and attraction of students to computer science.
IPRE’s program contains five main components: curriculum, community, assessment,
hardware and software:
11
A curriculum that can be quickly and easily adapted by others to suit their needs.
An open community for sharing idea and supporting each other.
Assessments of the impact and effectiveness of the tools that IPRE develops and
deploys.
Development and deployment of low cost robot for teachers and students.
An easily accessible software environment for students to learn to program and
for teachers to teach the curriculum.
[9]
Utilizing these components, IPRE has run several studies with Robots and CS1 at various
colleges using Myro and Scribbler robots.
2.2 Myro and Scribblers
My Robotics (Myro), is an open-source library and application programming interface aimed at
making it easy for beginners to learn about computer science by programming robots. Myro has
been implemented in several programming languages, including: Python, C++, C, Java, Ruby
and Scheme. Myro is an excellent tool for controlling both real and virtual Scribbler robots.
[14]
Scribblers are inexpensive differential
7
steer robots, manufactured by Parallax, that make
an ideal platform for people to learn about robots and computer science. They include enough
built-in sensors to perform a variety of common robot behaviors out-of-the-box including line-
following, obstacle avoidance, and light seeking. Scribblers have two infrared detectors on the
bottom of the robot, and two infrared distance or obstacle detector on the front left and right of
the robot, that give it the ability to check the left and right sides of the robot for obstacles and
7
A differential wheeled robot is a mobile robot whose movement is based on two separately driven
wheels placed on either side of the robot body. It can thus change its direction by varying the relative rate
of rotation of its wheels and hence does not require an additional steering motion. Unlike the wheels on a
car, for instance, they cannot change angles.
12
react accordingly (see Figures 2.1 and 2.2). The robots also have three photocell detectors, or
light sensors, on the front side of the robot, and a speaker for tone and melody generation.
[21]
The Scribblers are controlled with computers through a Bluetooth connection to another
piece of hardware that is attached to the Scribblers, the Fluke. The Fluke and Fluke2 (like the
Scribblers, there is a second version) were also designed by IPRE and are connected to the
Scribblers via the serial programming port. The Flukes have a camera, three IR emitters, one IR
receiver, and a Bluetooth transmitter and receiver (see Figure 2.3). It draws its power directly
through the programming port.
[19]
Additionally, Scribblers come with a simple gamepad that can
be used to control the movement of the robot. The controls on the gamepad are pre-programmed,
but can be modified by users.
Figure 2.2. Bottom view of Scribbler2 robot.
Figure 2.1. Top view of Scribbler2 robot.
13
2.3 Calico
IPRE’s latest interface for interacting with Myro and the Scribblers is Calico. Calico is a multi-
language, multi-context programming framework and learning environment for computing
education created by IPRE. Calico is completely free and open source, so anyone can download
the code and use it.
8
They can even edit the code and create modules, libraries and other useful
additions themselves. Calico comes with the Myro libraries pre-installed, thus there is no need
8
http://calico.codeplex.com/
Figure 2.3. Organization of the Fluke2.
14
to download and install them separately. It was designed to support several different
programming languages including Python, Scheme and Jigsaw, among others. Calico can be
used for a variety of contexts including scientific visualization, robotics, and art. It has a shell
window, output window and an integrated code editor. Calico also supports a variety of different
robots, such as the Scribblers.
[2]
Calico’s Myro comes with a built in simulator (see Figure 2.5). The simulator is still
under development, but allows a wide range of control over the robots and environment. The
simulator allows one to control a virtual Scribbler robot, with all of the same detectors and
sensors as the real robot has, including the camera. The color and arrangement of the virtual
world can be changed by adding objects or lights, and controlling the starting place of the robot
within the environment. The simulator can be controlled through code or the gamepad like the
Figure 2.4. Calico Interface
15
real Scribblers. The simulated scribblers can accomplish most anything that the real Scribblers
can, with the added functionality of being able to control multiple robots at once.
Python
9
is a widely used general-purpose
10
, high-level
11
programming language. It is an
object-oriented and structured programming language, meaning it makes extensive use of
subroutines (functions/methods), block structures and loops
12
. Python also comes with a large
and comprehensive standard library. Python was first started in the late 1980s, therefore there
are many great tutorials for learning and mastering the language, one reason that it is perfect for
9
http://www.python.org/
10
General purpose means that it does not have a specific deign purpose in mind, unlike a language such
as Structured Query Language, which is used solely for managing databases.
11
"High-level language" refers to the higher level of abstraction from machine language. Languages like
Java and C are both high-level languages as well.
12
More information on these concepts can be found in the Appendix (see Proposed Study Documents).
Figure 2.5. Scribbler simulator interface.
16
beginners.
[1]
Since Python is a dynamically-typed programing language
13
, it does not require
declaring object and variable types, making it a bit easier for people just learning to program. It
also has a plethora of additional libraries, or modules, that add or simplify features like game
design
14
, image manipulation
15
, web development
16
and many others. Python is a great language
for beginners and thus, an opportune language for the follow-up study, as well as IPRE’s first
choice for their studies.
2.4 IPRE Studies
Since IPRE believes that robots are an attractive way to introduce more students to computer
science, they have conducted several studies that aim to investigate this hypothesis.
[9]
Summet et
al (2009) details studies that IPRE ran in 2007 that involved teaching introductory computer
science classes at three different schools. These classes enrolled 178 students and were taught
using the personal Scribbler robots. Class sizes ranged from 12 to 104 students. The curriculum
material includes a textbook, lecture notes and slides, hardware and software, and assignments.
At the Georgia Institute of Technology, 90.97% (131 of 144 students) were successful (grade of
A, B, or C) in the Fall 2007 Robots class, consisting of both CS majors and non-majors. The
success rate in the Fall 2007 non-robots class, which consisted of non-majors, was 85.71% (78 of
91 students). The study found that on average the Robots students did 10% better than the Non-
Robots students.
The average annual enrollment in a Data Structures class at Bryn Mawr
College from 1995 to 2006 was 7.45 students. The 2007 and 2008 classes, after the introduction
13
Dynamically-typed programs verify the type safety of a program at runtime. C is a Statically-typed
language, the opposite of Dynamic.
14
http://www.pygame.org/news.html
15
http://www.pythonware.com/products/pil/
16
https://www.djangoproject.com/
17
of the introductory class with Robots, averaged 17.5 students, more than doubling the average
enrollment.
[23]
IPRE’s approach attempts to provide interesting and diverse ranges of examples and
exercises where the focus is on the context of the applications and not on the specific
programming features one has to master. This challenges students in unique ways, and can lead
students to pursue further studies in computing, which is one of the main goals of Hour of Code
and IPRE.
[12]
The studies that IPRE runs serve as the motivator for this project. First, as data to
examine and second, as a stepping stone for a future study involving real robots and simulated
robots, rather than robots versus non-robots.
18
3
RELATED WORK
This project spans several areas of study and there are many related articles of work. The main
topics include that of teaching and robotics. This project began with an aim towards a younger
audience than undergraduate level, thus many of the papers deal with teaching computer science
to children, but many key principles still apply, such as interfaces, and key topics and principles.
3.1 Teaching
Scratch is a visual programming environment that allows users (primarily ages eight to sixteen)
to learn computer programming while working on personally meaningful projects such as
animated stories and games. A key design goal of Scratch is to support self-directed learning
through tinkering and collaboration with peers, as well as introducing programming to those with
no previous programming experience. Scratch uses a visual block language, a single-window
user interface layout, and a minimal command set to simplify programming.
[15]
Scratch is a great
programming tool for children trying to learn about computer science. While the IPRE studies
and this senior project aim to be run with undergraduate-aged students, it was still very valuable
19
to read about what kinds of interfaces and problems work universally for teaching computer
science to an audience who has not had contact before.
Tucker et al. (2003) proposes a model curriculum that can be used to integrate computer
science fluency and competency throughout primary and secondary schools. This curriculum
model provides a four-level framework for computer science, and contains roughly the
equivalent of four half-year courses (many of these can be taught as modules, integrated among
existing science and mathematics curriculum units). The first two levels suggest subject matter
that ought to be mastered by all students, while the second two suggest topics that can be elected
by students with special interest in computer science, whether they are college-bound or not.
[25]
Their curriculum acclimatizes students to using technology, which is certainly an exceedingly
useful skill in today’s technology-driven world; a world that will probably only get more
technical. This was a very interesting paper to read, especially in regard to what topics they
deemed the most important; universal topics such as the steps in algorithmic problem-solving,
sequences, conditionals and loops (iteration).
Calico is another programming environment useful for new and experienced computer
scientists alike, and is discussed in more detail in Section 2.3.
[2]
Calico was designed with an
older audience in mind, and thus is the interface utilized in the proposed future studies of this
project. Calico provides a great, easy-to-use interface, and comes equipped with Myro, allowing
full control of the Scribbler robots, and the simulation interface.
As stated earlier, this project initially focused on a younger audience. In addition, while
IPRE currently only engages with undergraduate students, they also aim to be able to engage
with a wide range of ages. Therefore, it was worthwhile for this project to consider the worth of
teaching computer science to children. Is it really worth teaching young kids to code? Some
20
individuals think not. Mark Guzdial, a Professor in the School of Interactive Computing at the
Georgia Institute of Technology, is not convinced that you can “fruitfully teach five or six year
olds to code—though it’s certainly worth exploring and experimenting with.” He says that the
type of “coding” some young kids can learn is usually just sequences of statements. They rarely
contain things like loops or conditionals—important aspects of programming. He also argues
that teaching young kids the basics of programming has no effect long-term because the United
States curriculums do not really teach any computer science, so any knowledge they might have
acquired would be lost before they start learning computer science again at high school or
college level.
[7]
If the United States had something like the Tucker et al. (2003) curriculum
described above, perhaps it would be worthwhile. However, at this point, no such program is in
place. Such arguments, and current lack of structure to facilitate young computer scientists,
finalized the decision to focus this senior project on undergraduate students instead.
3.2 Robotics
Robots can be expensive, and are prone to noise, or errors, in their sensors and
movements. They can be broken or lost, and can require maintenance to stay in top condition.
However, they might be more engaging in a social context, and easier to physically influence
during runtime than simulations. Visual programming systems such as Alice
17
or robot
simulators such as Josef
18
, Karel
19
and Logo Turtle graphics
20
still live “in the computer”.
[23]
This means that you have to code in any changes you want to make to the robot or environment,
17
http://www.alice.org/index.php
18
http://machinarium.net/demo/
19
https://www.cs.mtsu.edu/~untch/karel/
20
http://el.media.mit.edu/logo-foundation/logo/turtle.html
21
and complicates making changes while a program is running. With real robots, you can interact
with them and the environment directly.
The IPRE studies argue in favor of using robots to teach computer science, and several
other studies seem to advocate their use. Correll et al. (2013) introduces a one-hour study for
middle school aged children to engage in robotics and computer science. This paper studies
using “Cubelets”
21
, a modular robotic construction kit, which requires virtually no setup time and
allows substantial engagement and change of perception of Science, Technology, Engineering
and Mathematics (STEM) in as little as a 1-hour session. This paper describes the constructivist
curriculum and provides qualitative and quantitative results on perception changes with respect
to STEM and computer science in particular as a field of study. The curriculum was designed to
teach students about several concepts at the core of computer science: decomposing problems
into sub-problems and composing solutions to partial problems to solve a larger problem.
[5]
Wyeth & Purchase (2013) use robots called Electronic Blocks
22
, which function very
much like the Cubelets. The blocks were made by placing electronics inside Lego Duplo
Primo
TM
blocks. Electronic Blocks are a new programming interface designed for children aged
between three and eight years. The programming environment includes sensor blocks, action
blocks and logic blocks. By connecting these blocks, children can program structures that
interact with the environment. The blocks provide young children with a programming
environment that allows them to explore quite complex programming principles. The simple
syntax of the blocks allows children to create and use simple code structures. The Electronic
Block environment provides a developmentally appropriate environment for planning overall
strategies for solving a problem, breaking a strategy down into manageable units, and
21
https://www.modrobotics.com/cubelets
22
http://itee.uq.edu.au/~peta/_ElectronicBlocks.htm
22
systematically determining the weakness of the solution. Electronic Blocks are the physical
embodiment of computer programming. They have the unique dynamic and programmable
properties of a computer minus its complexity.
[26]
It was amazing what these researchers could
accomplish with such young children, and again iterated the important things to teach in
beginning computer science: the strategies for breaking down and solving problems.
Martin (2007) explores challenges for both engineering and AI educators as robot toolkits
evolve. He talks a lot about the importance of feedback in robotics, and that students need to get
used to utilizing this feedback.
[16]
The feedback issue could be something that makes real robots
better to use than simulated ones, an aspect of the proposed follow-up study of this project.
3.3 Robots as Motivators
Despite the results that IPRE found, other studies have concluded that robots have little effect
over computer science attitudes and perceptions. The purpose of the study done by McGill
(2012) was to determine whether using the Scribbler motivates students to learn programming in
the form of a course with pre and post attitudes surveys, like the IPRE studies.
The study designed the surveys to study changes in opinions on attention, relevance,
confidence and satisfaction, all parts of computer science attitudes and perceptions. The results
of this study indicate that the use of these robots had a positive influence on participants’
attitudes towards learning to program, but little or no effect on relevance, confidence, or
satisfaction. Students were only slightly satisfied using the robots. This study indicates that other
than increasing attention initially, there is no clear positive effect on motivation when using
robots in an introductory programming course designed for non-majors. Robots in the classroom
motivate students by sparking their interest, but do little else.
[18]
23
3.4 An Hour versus a Semester
To recap, the Hour of Code is an opportunity for every student to try computer science for one
hour. It has tutorials and ideas for people of all ages to try, that cover a wide variety of activities:
game and app designing, robotics, creating cards and graphics, et cetera.
Hour of Code is not asking people to sign over their lives to computer science, they are
simply asking people to try it. Only an hour of their time—a small commitment to many. Small
enough that many people would agree to try it. However, is one hour long enough to change
perceptions? Did people actually learn anything in one hour?
Hour of Code does have a “Beyond One Hour” section
23
, filled with things to continue
learning about computer science, so they obviously believed that some people would be
interested enough to want to keep learning. In addition, if they were interested enough to keep
learning, then their perceptions of computer science must have shifted towards the positive as
well. Or at least they assume that. Corell et al. (2013) had hour-long sessions or studies in
which they taught computer science, and also had positive results. Students showed a significant
increase in interest to pursue computer science as a field of study after only an hour.
[5]
Due to their design considerations and apparent successes, the proposed follow-up study
is designed as hour-long coding workshops, as opposed to a semester-long course. Hour of Code
also focused heavily on simulated or virtual applications; controlling virtual, simulated avatars
(such as angry birds, snowmen, dogs, and of course, robots
24
), instead of having participants
using real robots. The proposed follow-up design aims to study the pros and cons of this
approach, and if Hour of Code could have been more (or less) successful with real robots, despite
23
https://code.org/learn/beyond
24
http://csedweek.org/learn
24
complications in accessibility the necessity of real robots creates. Thus, the goal of studying real
robots versus simulated robots.
3.5 Robot Simulations
Robot simulations allow users to create applications for a robot without depending physically on
the actual robot. Prensky (2013) classifies simulations into two “fidelity” categories based on
how closely these simulated robots emulate their physical counterparts. There are “low fidelity”
and “high fidelity” simulations. Low fidelity simulations are situations where one or a few
elements are abstracted from reality to be emphasized. High fidelity simulations attempt to
model reality as closely as possible.
[20]
The robot simulations that would be used in the follow-
up study would be classified somewhere in between high fidelity and low fidelity. While the
usage of the robot is high fidelity, the world that it exists in is not. The simulator looks different
and is immune to noise (errors in the sensor readings/movements).
Robot simulations have also been used as a motivator for students to pursue computer
science. The robot adventure game, outlined in Lee & Howard (2008), attempts to capitalize on
the popularity of computer games to teach younger students basic computer science concepts and
increase the students desire to pursue STEM-related careers in the future.
[13]
Mazur & Kuhrt
(1997) provides a simulation platform for student creativity in programming, interaction between
software modules, student cooperation and competition and classroom presentation of student
efforts. They found that these extra additions can lead to greater student interest which inspires
further discussion and learning.
[17]
Simulations are much less expensive than robots, and have no noise error. They cannot
be lost or broken, and do not require maintenance. You also do not need to have a separate copy
for each student, unlike with real robots.
[29]
You can just download software and make it freely
25
available to a whole class. However, simulated robots could be less engaging in a social context,
more isolated. It is also harder to physically influence the robot and its environment during
runtime.
The results analyzed previously show that robots do not have much influence over
attitudes, but perhaps simulations would be a better tool for changing perceptions and increasing
learning/enjoyment of the students. The proposed follow-up study aspires to address these
questions, and makes use of the Scribbler robots, also used in the IPRE studies.
26
4
STUDY ANALYSIS
Following the study in Summet et al (2009), IPRE ran additional studies at the University of
Tennessee, Knoxville (UTK) in the Fall semester of 2009 and Spring semester of 2010. Like the
Summet et al (2009) study, results were gathered from both Robotics classes and Non-Robotics
classes.
4.1 Robotics Class
The Robots class was an introductory (100-level) computer science course that utilized robots.
The course had no prerequisites and was open to all students. The course covered problem
solving and algorithm development, organization and characteristics of modern digital computers
with emphasis on software engineering, building abstractions with procedures and data, and
programming in a modern computer language. It included design projects that required
laboratory work, which focused on programming robots, specifically IPRE’s Scribbler Robots.
The course was taught in the C++ programming language and follows the IPRE
developed curriculum, as in Summet et al (2009). Students were asked to obtain these texts as
27
well: Learning Computing with Robots in C++, translated from Python by Deepak Kumar, and
How to Think Like a Computer Scientist: Learning with C++, by Allen B. Downey.
It covered topics such as: basic programming concepts, C++ syntax and semantics,
object-oriented programming, program development, sorting and searching, and robotics and AI.
The full course syllabus can be found in the Appendix (see IPRE Documents).
[11]
4.2 Non-Robotics Class
The Non-Robots class was also an introductory (100-level) computer science course. The course
covered all of the same areas listed above, without utilizing robots to teach the concepts. This
course also used the same How to Think Like a Computer Scientist: Learning with C++ textbook
as the Robots class. The Non-Robots class also covered the same introductory topics as the
Robots course.
The courses were designed to be as similar as possible, with the only major
difference being the utilization of the robot in the Robot class and the instructor teaching the
course.
[6]
4.3 Methodology
The data in this study were gathered as in the Summet et al (2009) studies. At the start of the
semester, students were given a pretest survey to fill out to gauge initial computer science
perceptions and attitudes. At the very end of the class, students were asked to fill out a posttest
survey to study any changes made in attitudes and perceptions over the semester. The questions
for all four survey conditions can be found in the Appendix (see IPRE Documents).
4.4 Results
All statistics were computed using the R Project for Statistical Computing
25
. Descriptive
statistics were used to analyze the responses to the Likert scale questions. Likert responses were
25
http://www.r-project.org/
28
coded into values ranging from one (strongly disagree) to five (strongly agree) for statistical
testing. The Mann-Whitney's U test was used to evaluate relationships between the four groups:
Pretest, Posttest, Robots and Non-Robots. The remainder of this section describes the results.
Robots
Non-Robots
Robots Pretest
n = 22
Non-Robots Pretest
n = 14
Robots Posttest
n = 27
Non-Robots Posttest
n = 9
4.4.1 General Opinions and Observations
Mostly Computer Science majors and other sciences (Engineering, Physics, Math, etc.) enrolled
in the two courses. Though, only two students rated themselves as being of intermediate
programing experience. The rest were either beginner or had none. Students were also
predominantly male and Caucasian. Out of all of the participants, only one had programmed a
robot before.
Generally, students in the Non-Robots class (µ = 3.22) seemed to enjoy themselves a bit
more than those in the Robots class (µ = 3.07) [Figure 4.8]. Students in the Robot class also
liked being able to access the textbooks online (µ = 4.074) instead of just having access to
physical copies [Figure 4.13]. Both groups felt like they knew more about computers after
completing the course than they did before taking them. [Figure 4.18]. However, in the Robots
class, confidence in problem solving decreased slightly from Pretest to Posttest, while in Non-
Robots it increased slightly [Figure 4.19].
Both Robots and Non-Robots also saw a decrease in seeking help for the courses between
the Pretest and Posttest [Figure 4.25]. In Pretest and Posttest surveys, students in both groups
Figure 3.1. Punnett Square of study groups.
29
also indicated that they liked technology and regularly tried out new experiences and products
[Figure 4.31] [Figure 4.32].
4.4.2 Statistically Significant Group Differences
Several of the questions had significant differences between groups. Results show that students
in the Non-Robots (µ = 3.89) course were more likely to write programs during the class that
were not assignments than students in the Robots (µ = 2.89) class (p = .08) [Figure 4.2]. Results
also showed that students in the Robots class were much more likely to discuss assignments and
lectures with peers who took the class (µ = 4.07, p = .01) [Figure 4.6]. Robots students were
also more likely to discuss the course material with peers who did not take the class (µ = 4.07, p
= .10) compared with students in the Non-Robots class (µ = 3.67) [Figure 4.7].
Students in the Non-Robots class (µ = 4.38) showed more enjoyment in being challenged
by seemingly unsolvable situations or problems than the Robots (µ = 3.67) class (p = .04), and
showed an increase in enjoyment in being challenged between the Pretest (µ = 3.5) and Posttest
(µ = 4.38) as well (p = .02) [Figure 4.22]. In the pretest, students in the Non-Robots class (µ =
4.14) started out more likely to ask for help than the Robots (µ = 3.64) class (p = .09) [Figure
4.26]. Additionally in the pretest, Non-Robots students (µ = 3.79) started out thinking that other
students in class knew more about computers than they did, than compared to the students in the
Robots (µ = 3.14) class (p = .07) [Figure 4.29].
4.4.3 Summary
Perhaps then, robots are not the answer. As found in McGill (2012), the robots seemed to have
little effect over students’ attitudes and perceptions of computer science. Nor did they seem to
be a more enjoyable teaching method than standard computer science. Overall, students seemed
to enjoy the Non-Robots class better. However, there were significantly less students in the
30
Non-Robots courses than the Robots courses, so that should be taken into account. Different
instructors also taught the two courses, which could have influenced students’ learning and
enjoyment of the course. Another possibly influencing factor was the decision to teach both of
the courses in C++, as compared to McGill (2012)’s choice of Python. Python is generally
considered a much easier language for beginners.
[22]
While there were significant differences in the Non-Robots, Pretest and Posttest groups,
there were no significant differences found in the Robots group; that is, between the Robot
Pretest and Robot Posttest. The only significant positive effect the robots seemed to have was
that students were much more likely to discuss their assignments and lectures with other
students, meaning the Robots class was more socially inclined than the Non-Robots. This is a
particularly interesting result, and definitely positive in effect. If the goal of introductory
computer science courses is to help spread the word about computer science (like the Hour of
Code), than using robots certainly helps that cause, as people are more likely to speak about it to
other people and increase the spread of awareness. Being more socially inclined, the robots
also pose a beneficial tool to introductory classes, since new students can do well with a social
support system behind them, something that many students feel lacking in when first beginning
computer science.
[18]
31
Figure 4.1. Survey Results for
Question 1: “My experiences
in this class caused me to
decide to take another
computer science class.”
Figure 4.0. Gender
Distribution of all groups.
32
Figure 4.3. Survey Results for
Question 3: “I expect that I
will have to write a program
(in any language) after I finish
this class.”
Figure 4.2. Survey Results for
Question 2: “During the class,
I wrote a program that was
not an assignment for this
class.”
33
Figure 4.5. Survey Results for
Question 5: “What I learned
in this class is important to my
future career.”
Figure 4.4. Survey Results for
Question 4: “There was at
least one homework that I
spent extra time on because I
thought it was cool.”
34
Figure 4.6. Survey Results for
Question 6: “I discuss difficult
assignments and/or detailed
lectures with friends in the
class.”
Figure 4.7. Survey Results
for Question 7: “I talk with
my friends (not in the class)
about the class.”
35
Figure 4.9. Survey Results for
Question 9: “I would have
liked to use robots in this
class.”
Figure 4.8. Survey Results for
Question 8: “I enjoyed this
class.”
36
Figure 4.11. Survey Results
for Question 11: “Maintaining
the robot was easy.”
Figure 4.10. Survey Results
for Question 10: “I enjoyed
using the robot in this class.
37
Figure 4.12. Survey Results
for Question 12: “The pace of
the lectures was too fast.”
Figure 4.13. Survey Results for
Question 13: “I liked being
able to read the textbooks
online.”
38
Figure 4.15. Survey Results
for Question 15: “I purchased
hardcopy of Learning
Computing with Robots.”
Figure 4.14. Survey Results
for Question 14: “I did all the
assigned readings.”
39
Figure 4.17. Survey Results
for Question 17: “I enjoyed
being able to take the robot
home with me.”
Figure 4.16. Survey Results
for Question 16: “I purchased
hardcopy of How to Think Like
a Computer Scientist.”
40
Figure 4.19. Survey Results
for Question 19: “I am
confident in my problem
solving ability.”
Figure 4.18. Survey Results
for Question 18: “Compared
to students in this class, I feel
I know a lot about
computers.”
41
Figure 4.21. Survey Results
for Question 21: “I am
confident in my ability to meet
unexpected challenges with
success.”
Figure 4.20. Survey Results
for Question 20: “I am
confident in my ability to
persist until a solution is
found.”
42
Figure 4.23. Survey Results
for Question 23: “I am
confident in my math ability.”
Figure 3.22. Survey Results
for Question 22: “I enjoy
being challenged by
seemingly unsolvable
situations or problems.”
43
Figure 4.24. Survey Results
for Question 24: I am
confident in my science
reasoning ability.”
Figure 4.25. Survey Results
for Question 25: “I seek help
in class before I become very
frustrated.”
44
Figure 4.27. Survey Results
for Question 27: “Computer
Science and programming
are the same thing.”
Figure 4.26. Survey Results
for Question 26: “I am not
afraid to ask for help.”
45
Figure 4.28. Survey Results
for Question 28: “I took this
course to see what computer
science is all about.”
Figure 4.29. Survey Results
for Question 29: “Other
students in class know more
about computers than I do.”
46
Figure 4.30. Survey Results
for Question 30: “I dislike
situations or problems that
are seemingly unsolvable.”
Figure 4.31. Survey Results
for Question 31: “I like
technology.”
47
Figure 4.33. Survey Results
for Question 33: “I am
confident programming in
C++.”
Figure 4.32. Survey Results
for Question 32: “I regularly
try out new experiences and
products.”
48
5
MOVING FORWARD
This project proposes a design and implementation of computer science coding workshops that
would focus on changing computer science attitudes and perceptions of its participants. The
coding workshops would be an hour-long introduction to Calico, Python and controlling a
Scribbler robot—either real or virtual, through the use of the Myro simulator. In this hour of
time, participants would work on two programming tasks, each of which can be graded in terms
of correctness, designed to make use of common programming principles and tactics. By the end
of the workshop, participants would hopefully have a basic understanding of Python, Calico and
several general programming concepts. This study aims to understand the differences between
using real robots to introduce computer science as opposed to using virtual, simulated robots as
an introduction to computer science. It also aspires to gauge whether computer science attitudes
and perceptions could be changed within the span of an hour-long workshop, instead of over an
entire semester.
49
5.1 Methodology
Testing participants would be solicited from within the Bard community. Although the test pool
would be limited to current Bard students at least eighteen years of age and people with little or
no programing background, no effort would be made to specifically target participants from
certain demographics. The expectation is that the final group of participants would contain
members from mixed demographics.
Time (minutes)
Activity
0-15
Consent Forms and Pretest Survey
15-25
Introduction
25-45
Task One: Dance Routine
45-75
Task Two: Light Finder
75-90
Posttest Survey
Each participant would be asked to fill out a Consent Form and a Pretest Survey, which
aims to garner what kind of, if any, computer science background each participant has had, and
to establish their current views of computer science and technology. Participants would then be
paired up with a partner to participate in the workshop. The workshop would be partitioned into
three sections: the introduction, first task and second task. After the workshop, each participant
would be asked to fill out a Posttest Survey, which aims to assess whether participants original
perceptions of computer science changed at all during the workshop. Depending on which trial
the participants are in, there would also be questions asking about their experience with either the
simulations or real robots, and if they thought the opposite would have been more interesting or
engaging. The entire process was designed to take an hour and a half; an hour for the workshop,
and fifteen minutes before and after to fill out the surveys and consent form. There are two
different conditions: a real robot trial and a simulator robot trial.
Figure 5.1. Study Schedule
50
5.2 Introduction
The introduction section is designed to take ten minutes. Examples would be shown either using
the simulator or a real robot, depending on which trial condition the group is in. It follows a
series of PowerPoint slides, which illustrate the basics of the Calico program and Python. The
next slides cover basic commands that participants would be using in the workshop, and how to
use them. These slides are followed by an introduction to Loops and Conditionals. The last part
of the introduction details the two programming tasks that the participants would be asked to
complete, and the introduction ends. The participants would then begin the first task.
5.3 First Task
The participants would be given twenty minutes to work on the first task. The first programming
task is designed to familiarize participants with using Calico and Python. Their task is to create a
simple dance sequence for their robot, which should last at least twenty seconds. This task was
designed to be easy and fun, but also introduce participants to the programming concepts of
sequences and program structure, and encourage them to make use of loops. A solution that runs
for twenty seconds using a loop structure is graded as a success condition. Partial success is
given for programs that use basic sequential commands that add up to twenty seconds worth of
behavior.
5.4 Second Task
The participants would be given thirty minutes to work on the second task. The second
programming task is designed to introduce participants to higher-level concepts and breaking
down problems. Their task is to make the robot drive around and try to locate a light source in
the environment. Once it does, the robot should beep to let the participant know the light has
been found. This task is designed to be a bit more difficult, and makes use of more aspects of the
51
robot, besides movement. Participants would have to use the light sensors, beep function,
relational operators (>, <, ==, !=), the Boolean data type, and would be encouraged to use both
loops and conditionals to solve this task. A solution that finds the light, and uses both loops and
conditionals would be graded as a complete success condition. Partial success would be given
for using either loops or conditionals but not both.
All study materials can be found in the Appendix (see Proposed Study Documents).
52
6
CONCLUSION
This project has presented an examination and analysis of the Institute for Personal Robots in
Education studies of 2009 and 2010 that judged students attitudes of computer science before
and after an introductory computer science course. The course was taught in one of two ways,
with robots or without them. The results from these studies show that the robots did not seem to
have any significant effects on computer science perceptions. However, the robots did increase
the likelihood that students discussed their assignments and lectures with their peers, a factor that
could lead to increasing involvement in computer science.
This project then detailed a follow-up study that could be run as an extension of the IPRE
studies, examining the effects of using real robots versus simulated robots. This study is
designed to address two research questions. The differences in robots and simulated robots on
changing computer science perceptions, and if an hour-long workshop is enough time to change
said perceptions.
Many complaints from McGill (2012) and the IPRE study short answer results were
about the robot hardware—batteries, not being calibrated correctly, not driving straight, faulty
53
sensors—the list goes on. Perhaps the aversion to the robots has less to do with a lack of interest
in the subject, and rather the irritations of additional—sometimes faulty—hardware. If this is the
case, simulated robots could be the happy medium of the two—Robots without hardware
upkeep. In order to tell if students would still just prefer no robots at all—simulated or not—
another test would have to be done comparing the utilization of simulated robots and no robots in
general, assuming that is, that there are some differences between using robots and simulated
robots.
54
APPENDIX
The following pages present the documents, tables and figures that compose the methodology
and results of the IPRE study and proposed follow-up study.
55
IPRE Results
Question Key
Q1 My experiences in this class caused me to decide to take another computer science class.
Q2 During the class, I wrote a program that was not an assignment for this class.
Q3 I expect that I will have to write a program (in any language) after I finish this class.
Q4 There was at least one homework that I spent extra time on because I thought it was cool.
Q5 What I learned in this class is important to my future career.
Q6 I discuss difficult assignments and/or detailed lectures with friends in the class.
Q7 I talk with my friends (not in the class) about the class.
Q8 I enjoyed this class.
Q9 I would have liked to use robots in this class.
Q10 I enjoyed using the robot in this class.
Q11 Maintaining the robot was easy.
Q12 The pace of the lectures was too fast.
Q13 I liked being able to read the textbooks online.
Q14 I did all the assigned readings.
Q15 I purchased hardcopy of Learning Computing with Robots.
Q16 I purchased hardcopy of How to Think Like a Computer Scientist.
Q17 I enjoyed being able to take the robot home with me.
Q18 Compared to students in this class, I feel I know a lot about computers.
Q19 I am confident in my problem solving ability.
Q20 I am confident in my ability to persist until a solution is found.
Q21 I am confident in my ability to meet unexpected challenges with success.
Q22 I enjoy being challenged by seemingly unsolvable situations or problems.
Q23 I am confident in my math ability.
Q24 I am confident in my science reasoning ability.
Q25 I seek help in class before I become very frustrated.
Q26 I am not afraid to ask for help.
Q27 Computer Science and programming are the same thing.
Q28 I took this course to see what computer science is all about.
Q29 Other students in class know more about computers than I do.
Q30 I dislike situations or problems that are seemingly unsolvable.
Q31 I like technology.
Q32 I regularly try out new experiences and products.
Q33 I am confident programming in C++
56
Tables
Robot Pretest Survey Results
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33
1 3 3 3 2 5 4 3 4 3 1 5 4 4 2
3 4 4 4 4 4 4 4 4 3 3 3 3 4 4
2 3 2 3 3 3 2 3 2 3 3 4 4 4 3
5 5 5 5 5 5 5 5 5 3 5 1 3 5 5
4 4 4 4 4 2 3 3 4 4 4 2 3 5 5
3 5 4 3 3 3 5 3 3 3 5 4 3 5 5
5 5 5 5 5 4 4 4 3 2 1 2 5 5
4 5 5 5 5 5 5 4 4 2 1 2 3 5 5
2 4 5 4 5 3 3 5 5 2 3 4 1 5 5
3 3 3 3 3 4 3 4 4 2 4 5 4 4 4
2 5 4 4 4 5 5 4 4 2 2 4 2 1 3
1 4 4 4 3 5 5 3 3 4 3 4 3 4 4
5 5 5 5 5 5 5 3 3 3 3 1 1 5 5
3 5 4 4 4 3 5 4 2 3 2 3 2 5 4
1 4 4 4 4 5 5 5 5 3 1 5 1 4 4
3 5 5 4 5 4 4 4 4 3 3 3 1 4 3
2 4 5 4 4 5 5 4 3 2 2 3 4 4 3
3 4 5 4 4 5 5 4 4 4 2 3 4 4 3
4 5 5 4 4 5 5 4 4 3 3 2 2 5 4
3 5 4 4 5 5 5 4 4 3 2 2 2 5 4
3 4 5 4 5 4 4 4 4 2 2 3 2 5 5
2 3 4 4 2 4 4 2 2 3 4 3 4 5 4
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33
Min 1 3 2 3 2 2 2 2 2 2 1 1 1 1 2
1st Qu 2 4 4 4 3.25 4 4 3 3 2 2 2 2 4 3.25
Median 3 4 4 4 4 4.5 5 4 4 3 3 3 3 5 4
Mean 2.909 4.273 4.273 4 4 4.227 4.318 3.773 3.636 2.818 2.682 3.143 2.636 4.409 4.045
3rd Qu 3.75 5 5 4 5 5 5 4 4 3 3 4 3.75 5 5
Max 5 5 5 5 5 5 5 5 5 4 5 5 4 5 5
SD 1.230915 0.7672969 0.8270325 0.6172134 0.9759001 0.9223065 0.8937009 0.7516216 0.9021379 0.6644986 1.210524 1.195229 1.093071 0.9081164 0.8985318
Responses 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22 22 22 22 22 22 22 22 22 22 22 21 22 22 22 0
57
Robot Posttest Survey Results
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33
4 5 5 5 5 4 4 3 1 4 3 5 5 1 1 3 4 4 4 4 4 4 4 4 4 3 2 1 2 5 5 5
2 3 3 3 4 4 2 3 3 4 2 4 2 2 2 4 2 3 4 3 3 4 3 2 3 3 2 4 4 4 3 2
4 2 2 4 5 4 4 4 4 4 3 4 2 3 3 4 3 4 4 4 4 4 4 4 4 3 4 4 4 5 4 3
2 2 4 3 4 5 5 4 4 4 3 4 2 2 2 3 3 4 4 4 4 5 5 4 4 3 3 3 3 5 5 3
1 1 2 1 1 5 4 1 1 2 3 5 5 1 1 1 2 4 4 4 3 4 3 4 5 1 1 3 3 4 3 1
3 5 5 3 4 2 4 3 3 4 3 4 4 1 1 3 2 3 4 4 3 3 3 2 2 3 5 4 3 4 3 2
4 5 5 2 5 5 4 4 1 1 1 4 1 1 1 1 4 5 4 5 4 5 5 5 5 4 1 2 3 5 4 4
4 2 4 4 4 4 4 4 4 4 4 5 3 1 1 4 2 3 4 4 4 4 4 2 3 4 4 4 3 5 5 3
3 3 3 2 4 3 3 3 3 4 3 3 2 2 2 3 3 3 3 3 2 3 2 4 4 3 2 3 4 3 3 2
2 5 5 5 5 5 5 4 1 1 2 5 4 1 1 1 4 5 4 5 4 5 5 4 4 1 1 2 2 5 5 4
1 4 4 1 1 4 4 1 1 1 2 4 2 1 1 2 2 5 5 5 4 5 5 2 4 5 1 4 4 2 2 1
3 2 5 3 2 4 4 3 2 3 3 4 1 5 5 5 3 3 3 3 2 2 3 3 3 2 4 2 4 5 4 2
4 3 2 4 3 3 4 4 4 3 3 2 4 4 4 2 3 3 4 4 4 3 3 4 4 2 4 3 2 4 4 2
2 4 4 4 4 4 4 3 4 4 2 4 4 2 2 3 4 4 4 4 4 4 4 4 4 3 2 3 2 4 4 3
3 1 4 2 3 2 2 3 3 2 3 2 2 1 1 2 3 5 4 3 4 5 5 2 4 2 2 3 3 4 4 3
1 1 4 2 3 5 4 1 3 5 5 5 4 1 1 5 3 5 5 5 3 4 4 4 4 4 3 3 4 5 4 3
1 4 4 5 5 5 5 4 3 4 1 5 1 1 1 1 5 5 5 4 5 4 5 3 3 3 4 2 1 5 5 3
1 1 2 2 3 4 4 2 3 3 3 5 3 1 1 3 5 5 5 5 5 5 5 4 4 4 2 2 1 5 5 3
4 3 3 4 4 5 5 5 5 5 5 4 1 1 1 5 4 5 5 5 5 5 5 3 3 3 2 4 3 4 4 4
2 1 4 3 4 4 4 2 4 1 2 4 3 1 1 1 2 4 4 3 4 5 5 2 1 2 2 4 3 4 3 2
5 5 5 5 5 5 5 5 5 5 3 5 4 1 1 5 4 5 5 5 5 4 5 2 4 2 3 2 1 5 5 5
1 2 4 2 3 4 4 1 1 4 4 2 4 2 2 4 2 4 4 4 5 4 5 2 4 3 1 4 1 4 4 1
4 3 3 4 4 4 4 4 4 4 4 4 3 2 2 3 4 4 4 4 4 5 5 4 5 4 4 4 4 5 4 4
1 1 5 1 3 2 4 1 2 2 3 3 2 1 1 1 1 1 1 1 1 4 3 2 3 4 2 5 5 3 1 1
2 5 5 5 2 5 5 4 3 3 4 5 2 1 1 4 5 5 4 5 2 1 5 5 5 5 4 3 5 5 5 4
4 2 4 4 5 4 4 4 4 5 1 4 2 2 2 4 3 4 4 4 4 5 5 4 4 2 2 3 2 5 4 4
2 3 5 4 3 5 5 3 3 2 3 5 2 5 1 4 4 4 4 4 3 5 5 5 5 3 3 2 3 4 4 3
58
Non-Robot Pretest Survey Results
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q 14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q 28 Q29 Q30 Q31 Q32 Q33
1 4 4 4 3 4 4 2 4 2 2 4 3 2 2
4 5 5 3 4 5 5 4 3 2 4 2 4 3
4 4 4 4 4 4 4 5 4 2 2 4 2 4 4
2 4 4 3 4 4 3 3 2 2 2 5 3 4 3
2 3 2 2 2 3 3 4 5 4 2 4 4 4 4
3 4 4 4 2 5 5 5 5 3 1 4 4 4 4
4 2 4 4 4 3 3 4 5 2 1 3 2 4 3
3 5 5 5 2 5 5 2 4 2 2 3 1 5 4
3 4 5 4 5 4 3 4 5 2 3 3 1 5 5
4 5 4 5 4 5 5 5 4 4 1 4 2 1 3
2 4 4 4 4 4 4 2 3 2 4 4 2 4 4
3 3 3 3 3 4 4 4 4 4 3 4 3 4 4
2 3 5 4 4 5 5 5 5 3 3 4 2 5 4
3 4 2 4 4 5 4 5 5 2 2 3 2 5 3
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33
Min 1 2 2 2 2 3 3 2 2 2 1 3 1 1 2
1st Qu 2 3.25 4 3.25 3 4 3.25 3.25 4 2 2 3.25 2 4 3
Median 3 4 4 4 4 4 4 4 4 2 2 4 2 4 4
Mean 2.857 3.857 3.929 3.786 3.5 4.286 4.071 3.857 4.143 2.571 2.154 3.786 2.357 3.929 3.571
3rd Qu 3.75 4 4.75 4 4 5 5 5 5 3 3 4 3 4.75 4
Max 4 5 5 5 5 5 5 5 5 4 4 5 4 5 5
SD 0.9492623 0.8644378 0.997249 0.8017837 0.9405399 0.726273 0.8287419 1.167321 0.9492623 0.8516306 0.898717 0.5789342 0.9287827 1.141139 0.7559289
Responses 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 14 14 14 14 14 14 14 14 14 13 14 14 14 14 0
59
Non-Robot Posttest Survey Results
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33
2 5 5 2 4 2 3 4 4 5 5 5 5 5 5 5 1 2 3 2 1 2 4 3
3 5 5 4 5 4 4 3 1 5 4 3 4 4 5 4 4 4 4 2 2 2 5 4
2 4 5 4 5 3 2 4 3 4 4 4 4 5 3 4 2 5 2 3 2 1 5 5
1 4 2 1 1 3 4 1 1 1 2 2 2 2 4 4 4 5 3 2 5 4 4 4
4 4 5 4 4 2 4 4 4 4 4 5 4 5 4 4 3 4 2 2 3 1 5 4
1 4 4 1 1 1 4 1 1 2 5 5 5 5 5 5 2 4 3 1 4 2 2 3
5 5 5 5 5 5 4 5 4 4 4 5 5 5 5 5 5 5 3 4 4 3 5 5
3 2 2 3 2 4 4 3 3 3 4 4 4 4 4 4 4 4 3 2 3 3 4 4
3 2 5 5 5 3 4 4 5
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33
Min 1 2 2 1 1 1 2 1 1 1 2 2 2 2 3 4 1 2 2 1 1 1 2 3
1st Qu 2 4 4 2 2 2 4 3 1 2.75 4 3.75 4 4 4 4 2 4 2.75 2 2 1.75 4 3.75
Median 3 4 5 4 4 3 4 4 3 4 4 4.5 4 5 4.5 4 3.5 4 3 2 3 2 4.5 4
Mean 2.667 3.889 4.222 3.222 3.556 3 3.667 3.222 2.889 3.5 4 4.125 4.125 4.375 4.375 4.375 3.125 4.125 2.875 2.25 3 2.25 4.25 4
3rd Qu 3 5 5 4 5 4 4 4 4 4.25 4.25 5 5 5 5 5 4 5 3 2.25 4 3 5 4.25
Max 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 4 4 5 4 5 5
SD 1.322876 1.166667 1.301708 1.563472 1.740051 1.224745 0.7071068 1.394433 1.536591 1.414214 0.9258201 1.125992 0.9910312 1.06066 0.7440238 0.5175492 1.356203 0.9910312 0.6408699 0.8864053 1.309307 1.035098 1.035098 0.7559289
Reponses 9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 0 0 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 0
60
IPRE Documents
Robots Course Syllabus
61
62
Proposed Study Documents
Documents Submitted for IRB Approval
Recruitment Poster
63
Consent Form
64
65
Pretest Survey
66
67
Robots Posttest Survey
68
69
Simulation Posttest Survey
70
71
Contact Form
72
Debriefing Form
73
Workshop Introduction Materials
Workshop Script
74
75
76
77
PowerPoint Slides
78
79
Workshop Task Materials
Workshop “Cheat Sheet”
80
81
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